Overview

Dataset statistics

Number of variables56
Number of observations15120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 MiB
Average record size in memory448.0 B

Variable types

Numeric12
Categorical44

Alerts

Soil_Type7 has constant value "0" Constant
Soil_Type15 has constant value "0" Constant
Elevation is highly correlated with Horizontal_Distance_To_Roadways and 2 other fieldsHigh correlation
Aspect is highly correlated with Hillshade_3pmHigh correlation
Slope is highly correlated with Hillshade_NoonHigh correlation
Horizontal_Distance_To_Hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with Horizontal_Distance_To_HydrologyHigh correlation
Horizontal_Distance_To_Roadways is highly correlated with ElevationHigh correlation
Hillshade_9am is highly correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly correlated with Slope and 1 other fieldsHigh correlation
Hillshade_3pm is highly correlated with Aspect and 2 other fieldsHigh correlation
Horizontal_Distance_To_Fire_Points is highly correlated with ElevationHigh correlation
Wilderness_Area1 is highly correlated with Soil_Type29High correlation
Wilderness_Area3 is highly correlated with Wilderness_Area4High correlation
Wilderness_Area4 is highly correlated with Elevation and 1 other fieldsHigh correlation
Soil_Type29 is highly correlated with Wilderness_Area1High correlation
Elevation is highly correlated with Horizontal_Distance_To_Roadways and 1 other fieldsHigh correlation
Aspect is highly correlated with Hillshade_9am and 1 other fieldsHigh correlation
Slope is highly correlated with Hillshade_NoonHigh correlation
Horizontal_Distance_To_Hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with Horizontal_Distance_To_HydrologyHigh correlation
Horizontal_Distance_To_Roadways is highly correlated with ElevationHigh correlation
Hillshade_9am is highly correlated with Aspect and 1 other fieldsHigh correlation
Hillshade_Noon is highly correlated with Slope and 1 other fieldsHigh correlation
Hillshade_3pm is highly correlated with Aspect and 2 other fieldsHigh correlation
Wilderness_Area1 is highly correlated with Soil_Type29High correlation
Wilderness_Area3 is highly correlated with Wilderness_Area4High correlation
Wilderness_Area4 is highly correlated with Elevation and 1 other fieldsHigh correlation
Soil_Type29 is highly correlated with Wilderness_Area1High correlation
Elevation is highly correlated with Wilderness_Area4High correlation
Horizontal_Distance_To_Hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with Horizontal_Distance_To_HydrologyHigh correlation
Hillshade_9am is highly correlated with Hillshade_3pmHigh correlation
Hillshade_3pm is highly correlated with Hillshade_9amHigh correlation
Wilderness_Area1 is highly correlated with Soil_Type29High correlation
Wilderness_Area3 is highly correlated with Wilderness_Area4High correlation
Wilderness_Area4 is highly correlated with Elevation and 1 other fieldsHigh correlation
Soil_Type29 is highly correlated with Wilderness_Area1High correlation
Soil_Type25 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type33 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type21 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type31 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type34 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type11 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type14 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type27 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Wilderness_Area1 is highly correlated with Soil_Type29 and 2 other fieldsHigh correlation
Wilderness_Area2 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type28 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type13 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type17 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type22 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type37 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type38 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type6 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type29 is highly correlated with Wilderness_Area1 and 2 other fieldsHigh correlation
Soil_Type20 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type36 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type19 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type30 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Wilderness_Area3 is highly correlated with Wilderness_Area4 and 2 other fieldsHigh correlation
Soil_Type8 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type18 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type5 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Wilderness_Area4 is highly correlated with Wilderness_Area3 and 2 other fieldsHigh correlation
Soil_Type1 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type26 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type40 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type12 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type35 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type2 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type15 is highly correlated with Soil_Type25 and 42 other fieldsHigh correlation
Soil_Type10 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type39 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type16 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type7 is highly correlated with Soil_Type25 and 42 other fieldsHigh correlation
Soil_Type32 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type9 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type23 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type24 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type4 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Soil_Type3 is highly correlated with Soil_Type15 and 1 other fieldsHigh correlation
Id is highly correlated with Elevation and 7 other fieldsHigh correlation
Elevation is highly correlated with Id and 12 other fieldsHigh correlation
Aspect is highly correlated with Hillshade_9am and 2 other fieldsHigh correlation
Slope is highly correlated with Hillshade_9am and 2 other fieldsHigh correlation
Horizontal_Distance_To_Hydrology is highly correlated with Elevation and 1 other fieldsHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with Horizontal_Distance_To_HydrologyHigh correlation
Horizontal_Distance_To_Roadways is highly correlated with Id and 5 other fieldsHigh correlation
Hillshade_9am is highly correlated with Aspect and 2 other fieldsHigh correlation
Hillshade_Noon is highly correlated with Aspect and 2 other fieldsHigh correlation
Hillshade_3pm is highly correlated with Aspect and 3 other fieldsHigh correlation
Horizontal_Distance_To_Fire_Points is highly correlated with Id and 5 other fieldsHigh correlation
Wilderness_Area1 is highly correlated with Id and 7 other fieldsHigh correlation
Wilderness_Area2 is highly correlated with ElevationHigh correlation
Wilderness_Area3 is highly correlated with Id and 4 other fieldsHigh correlation
Wilderness_Area4 is highly correlated with Id and 8 other fieldsHigh correlation
Soil_Type3 is highly correlated with Wilderness_Area4High correlation
Soil_Type8 is highly correlated with Soil_Type25High correlation
Soil_Type10 is highly correlated with Elevation and 1 other fieldsHigh correlation
Soil_Type18 is highly correlated with Horizontal_Distance_To_Fire_PointsHigh correlation
Soil_Type25 is highly correlated with Soil_Type8High correlation
Soil_Type29 is highly correlated with Id and 1 other fieldsHigh correlation
Soil_Type30 is highly correlated with Wilderness_Area1High correlation
Soil_Type38 is highly correlated with ElevationHigh correlation
Soil_Type39 is highly correlated with ElevationHigh correlation
Soil_Type40 is highly correlated with ElevationHigh correlation
Cover_Type is highly correlated with Id and 2 other fieldsHigh correlation
Id is uniformly distributed Uniform
Id has unique values Unique
Horizontal_Distance_To_Hydrology has 1590 (10.5%) zeros Zeros
Vertical_Distance_To_Hydrology has 1890 (12.5%) zeros Zeros

Reproduction

Analysis started2022-05-07 10:26:14.381861
Analysis finished2022-05-07 10:27:57.897219
Duration1 minute and 43.52 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Id
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct15120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7560.5
Minimum1
Maximum15120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2022-05-07T13:27:58.335056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile756.95
Q13780.75
median7560.5
Q311340.25
95-th percentile14364.05
Maximum15120
Range15119
Interquartile range (IQR)7559.5

Descriptive statistics

Standard deviation4364.91237
Coefficient of variation (CV)0.5773311779
Kurtosis-1.2
Mean7560.5
Median Absolute Deviation (MAD)3780
Skewness0
Sum114314760
Variance19052460
MonotonicityStrictly increasing
2022-05-07T13:27:58.834020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
100861
 
< 0.1%
100741
 
< 0.1%
100751
 
< 0.1%
100761
 
< 0.1%
100771
 
< 0.1%
100781
 
< 0.1%
100791
 
< 0.1%
100801
 
< 0.1%
100811
 
< 0.1%
Other values (15110)15110
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
151201
< 0.1%
151191
< 0.1%
151181
< 0.1%
151171
< 0.1%
151161
< 0.1%
151151
< 0.1%
151141
< 0.1%
151131
< 0.1%
151121
< 0.1%
151111
< 0.1%

Elevation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1665
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.322553
Minimum1863
Maximum3849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2022-05-07T13:27:59.223518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1863
5-th percentile2117
Q12376
median2752
Q33104
95-th percentile3397
Maximum3849
Range1986
Interquartile range (IQR)728

Descriptive statistics

Standard deviation417.6781873
Coefficient of variation (CV)0.151920402
Kurtosis-1.082115791
Mean2749.322553
Median Absolute Deviation (MAD)367
Skewness0.07563970694
Sum41569757
Variance174455.0682
MonotonicityNot monotonic
2022-05-07T13:27:59.627143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
283025
 
0.2%
229025
 
0.2%
337124
 
0.2%
295223
 
0.2%
295523
 
0.2%
282023
 
0.2%
324423
 
0.2%
279523
 
0.2%
285022
 
0.1%
296222
 
0.1%
Other values (1655)14887
98.5%
ValueCountFrequency (%)
18631
< 0.1%
18741
< 0.1%
18791
< 0.1%
18881
< 0.1%
18892
< 0.1%
18961
< 0.1%
18981
< 0.1%
18991
< 0.1%
19011
< 0.1%
19032
< 0.1%
ValueCountFrequency (%)
38492
< 0.1%
38481
< 0.1%
38462
< 0.1%
38441
< 0.1%
38421
< 0.1%
38391
< 0.1%
38361
< 0.1%
38311
< 0.1%
38271
< 0.1%
38252
< 0.1%

Aspect
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct361
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.6766534
Minimum0
Maximum360
Zeros110
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2022-05-07T13:28:00.037611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q165
median126
Q3261
95-th percentile344
Maximum360
Range360
Interquartile range (IQR)196

Descriptive statistics

Standard deviation110.0858014
Coefficient of variation (CV)0.7026305386
Kurtosis-1.150244484
Mean156.6766534
Median Absolute Deviation (MAD)77
Skewness0.450935294
Sum2368951
Variance12118.88367
MonotonicityNot monotonic
2022-05-07T13:28:00.471842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45117
 
0.8%
0110
 
0.7%
90109
 
0.7%
6389
 
0.6%
7687
 
0.6%
2782
 
0.5%
31581
 
0.5%
7580
 
0.5%
10879
 
0.5%
11778
 
0.5%
Other values (351)14208
94.0%
ValueCountFrequency (%)
0110
0.7%
148
0.3%
250
0.3%
354
0.4%
451
0.3%
546
0.3%
657
0.4%
748
0.3%
856
0.4%
951
0.3%
ValueCountFrequency (%)
3602
 
< 0.1%
35933
0.2%
35847
0.3%
35758
0.4%
35650
0.3%
35545
0.3%
35451
0.3%
35355
0.4%
35260
0.4%
35155
0.4%

Slope
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.5015873
Minimum0
Maximum52
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2022-05-07T13:28:00.821214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q110
median15
Q322
95-th percentile32
Maximum52
Range52
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.453926762
Coefficient of variation (CV)0.5123099134
Kurtosis-0.2383101358
Mean16.5015873
Median Absolute Deviation (MAD)6
Skewness0.5236583383
Sum249504
Variance71.4688777
MonotonicityNot monotonic
2022-05-07T13:28:01.320537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11740
 
4.9%
10739
 
4.9%
13717
 
4.7%
14699
 
4.6%
12677
 
4.5%
15664
 
4.4%
9664
 
4.4%
16640
 
4.2%
17598
 
4.0%
8574
 
3.8%
Other values (42)8408
55.6%
ValueCountFrequency (%)
05
 
< 0.1%
178
 
0.5%
2134
 
0.9%
3210
 
1.4%
4305
2.0%
5423
2.8%
6465
3.1%
7573
3.8%
8574
3.8%
9664
4.4%
ValueCountFrequency (%)
521
 
< 0.1%
501
 
< 0.1%
495
 
< 0.1%
481
 
< 0.1%
473
 
< 0.1%
4615
0.1%
453
 
< 0.1%
445
 
< 0.1%
432
 
< 0.1%
423
 
< 0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct400
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227.1957011
Minimum0
Maximum1343
Zeros1590
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2022-05-07T13:28:01.791022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q167
median180
Q3330
95-th percentile631
Maximum1343
Range1343
Interquartile range (IQR)263

Descriptive statistics

Standard deviation210.0752957
Coefficient of variation (CV)0.9246446774
Kurtosis2.803984388
Mean227.1957011
Median Absolute Deviation (MAD)120
Skewness1.488052491
Sum3435199
Variance44131.62986
MonotonicityNot monotonic
2022-05-07T13:28:02.419565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01590
 
10.5%
301207
 
8.0%
150497
 
3.3%
60490
 
3.2%
42452
 
3.0%
67411
 
2.7%
85381
 
2.5%
108361
 
2.4%
90284
 
1.9%
120283
 
1.9%
Other values (390)9164
60.6%
ValueCountFrequency (%)
01590
10.5%
301207
8.0%
42452
 
3.0%
60490
 
3.2%
67411
 
2.7%
85381
 
2.5%
90284
 
1.9%
95259
 
1.7%
108361
 
2.4%
120283
 
1.9%
ValueCountFrequency (%)
13431
< 0.1%
13181
< 0.1%
12941
< 0.1%
12612
< 0.1%
12602
< 0.1%
12181
< 0.1%
12131
< 0.1%
12081
< 0.1%
12031
< 0.1%
12011
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct423
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.07652116
Minimum-146
Maximum554
Zeros1890
Zeros (%)12.5%
Negative1139
Negative (%)7.5%
Memory size118.2 KiB
2022-05-07T13:28:03.251113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-146
5-th percentile-4
Q15
median32
Q379
95-th percentile176
Maximum554
Range700
Interquartile range (IQR)74

Descriptive statistics

Standard deviation61.23940613
Coefficient of variation (CV)1.198973711
Kurtosis3.403498704
Mean51.07652116
Median Absolute Deviation (MAD)32
Skewness1.53777568
Sum772277
Variance3750.264863
MonotonicityNot monotonic
2022-05-07T13:28:03.678064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01890
 
12.5%
5217
 
1.4%
3206
 
1.4%
4200
 
1.3%
8198
 
1.3%
7182
 
1.2%
10176
 
1.2%
9166
 
1.1%
2165
 
1.1%
6162
 
1.1%
Other values (413)11558
76.4%
ValueCountFrequency (%)
-1461
< 0.1%
-1341
< 0.1%
-1231
< 0.1%
-1151
< 0.1%
-1141
< 0.1%
-1101
< 0.1%
-1081
< 0.1%
-1041
< 0.1%
-1031
< 0.1%
-1002
< 0.1%
ValueCountFrequency (%)
5541
< 0.1%
5472
< 0.1%
4111
< 0.1%
4031
< 0.1%
4011
< 0.1%
3972
< 0.1%
3951
< 0.1%
3931
< 0.1%
3901
< 0.1%
3871
< 0.1%

Horizontal_Distance_To_Roadways
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3250
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1714.023214
Minimum0
Maximum6890
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2022-05-07T13:28:04.084893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile242
Q1764
median1316
Q32270
95-th percentile4635.1
Maximum6890
Range6890
Interquartile range (IQR)1506

Descriptive statistics

Standard deviation1325.066358
Coefficient of variation (CV)0.7730737525
Kurtosis1.022419366
Mean1714.023214
Median Absolute Deviation (MAD)690
Skewness1.247810678
Sum25916031
Variance1755800.854
MonotonicityNot monotonic
2022-05-07T13:28:04.403429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15088
 
0.6%
12056
 
0.4%
39047
 
0.3%
61845
 
0.3%
111043
 
0.3%
70041
 
0.3%
10838
 
0.3%
90037
 
0.2%
127337
 
0.2%
99037
 
0.2%
Other values (3240)14651
96.9%
ValueCountFrequency (%)
03
 
< 0.1%
3015
 
0.1%
425
 
< 0.1%
6011
 
0.1%
6713
 
0.1%
8510
 
0.1%
9023
0.2%
9519
 
0.1%
10838
0.3%
12056
0.4%
ValueCountFrequency (%)
68901
< 0.1%
68361
< 0.1%
68111
< 0.1%
67661
< 0.1%
66791
< 0.1%
66601
< 0.1%
65082
< 0.1%
64141
< 0.1%
64061
< 0.1%
63711
< 0.1%

Hillshade_9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct176
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.7042989
Minimum0
Maximum254
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2022-05-07T13:28:04.764231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile151
Q1196
median220
Q3235
95-th percentile250
Maximum254
Range254
Interquartile range (IQR)39

Descriptive statistics

Standard deviation30.56128689
Coefficient of variation (CV)0.143679686
Kurtosis1.218810484
Mean212.7042989
Median Absolute Deviation (MAD)18
Skewness-1.093680561
Sum3216089
Variance933.9922561
MonotonicityNot monotonic
2022-05-07T13:28:05.181575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226279
 
1.8%
229269
 
1.8%
224265
 
1.8%
228261
 
1.7%
230260
 
1.7%
233248
 
1.6%
223245
 
1.6%
219242
 
1.6%
231239
 
1.6%
225236
 
1.6%
Other values (166)12576
83.2%
ValueCountFrequency (%)
01
 
< 0.1%
581
 
< 0.1%
592
< 0.1%
651
 
< 0.1%
731
 
< 0.1%
781
 
< 0.1%
802
< 0.1%
811
 
< 0.1%
833
< 0.1%
852
< 0.1%
ValueCountFrequency (%)
254190
1.3%
253200
1.3%
252189
1.2%
251174
1.2%
250192
1.3%
249195
1.3%
248178
1.2%
247188
1.2%
246181
1.2%
245201
1.3%

Hillshade_Noon
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct141
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.9656085
Minimum99
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2022-05-07T13:28:05.607292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile175
Q1207
median223
Q3235
95-th percentile250
Maximum254
Range155
Interquartile range (IQR)28

Descriptive statistics

Standard deviation22.80196554
Coefficient of variation (CV)0.1041349174
Kurtosis1.153484179
Mean218.9656085
Median Absolute Deviation (MAD)14
Skewness-0.9532317075
Sum3310760
Variance519.9296327
MonotonicityNot monotonic
2022-05-07T13:28:06.062621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225327
 
2.2%
229324
 
2.1%
226320
 
2.1%
224313
 
2.1%
230311
 
2.1%
223303
 
2.0%
232298
 
2.0%
222297
 
2.0%
228294
 
1.9%
218293
 
1.9%
Other values (131)12040
79.6%
ValueCountFrequency (%)
994
< 0.1%
1021
 
< 0.1%
1031
 
< 0.1%
1071
 
< 0.1%
1112
< 0.1%
1133
< 0.1%
1141
 
< 0.1%
1151
 
< 0.1%
1161
 
< 0.1%
1181
 
< 0.1%
ValueCountFrequency (%)
254133
0.9%
253163
1.1%
252152
1.0%
251183
1.2%
250167
1.1%
249176
1.2%
248196
1.3%
247210
1.4%
246214
1.4%
245207
1.4%

Hillshade_3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct247
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.0919974
Minimum0
Maximum248
Zeros88
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2022-05-07T13:28:06.514186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile53
Q1106
median138
Q3167
95-th percentile207
Maximum248
Range248
Interquartile range (IQR)61

Descriptive statistics

Standard deviation45.89518871
Coefficient of variation (CV)0.3397328458
Kurtosis-0.08734390755
Mean135.0919974
Median Absolute Deviation (MAD)30
Skewness-0.3408272326
Sum2042591
Variance2106.368347
MonotonicityNot monotonic
2022-05-07T13:28:06.971325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143182
 
1.2%
149161
 
1.1%
132156
 
1.0%
133154
 
1.0%
142154
 
1.0%
136154
 
1.0%
137152
 
1.0%
138148
 
1.0%
154148
 
1.0%
152145
 
1.0%
Other values (237)13566
89.7%
ValueCountFrequency (%)
088
0.6%
11
 
< 0.1%
33
 
< 0.1%
41
 
< 0.1%
62
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
103
 
< 0.1%
112
 
< 0.1%
ValueCountFrequency (%)
2482
 
< 0.1%
2474
< 0.1%
2464
< 0.1%
2454
< 0.1%
2443
< 0.1%
2434
< 0.1%
2423
< 0.1%
2413
< 0.1%
2407
< 0.1%
2395
< 0.1%

Horizontal_Distance_To_Fire_Points
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct2710
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1511.147288
Minimum0
Maximum6993
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2022-05-07T13:28:07.388336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile296.9
Q1730
median1256
Q31988.25
95-th percentile3663.05
Maximum6993
Range6993
Interquartile range (IQR)1258.25

Descriptive statistics

Standard deviation1099.936493
Coefficient of variation (CV)0.7278817235
Kurtosis3.385415788
Mean1511.147288
Median Absolute Deviation (MAD)595
Skewness1.617098874
Sum22848547
Variance1209860.288
MonotonicityNot monotonic
2022-05-07T13:28:07.822183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61865
 
0.4%
54151
 
0.3%
63645
 
0.3%
60743
 
0.3%
96042
 
0.3%
57342
 
0.3%
75241
 
0.3%
94240
 
0.3%
24240
 
0.3%
34240
 
0.3%
Other values (2700)14671
97.0%
ValueCountFrequency (%)
02
 
< 0.1%
309
 
0.1%
4211
0.1%
6010
 
0.1%
6720
0.1%
858
 
0.1%
909
 
0.1%
9519
0.1%
10825
0.2%
1208
 
0.1%
ValueCountFrequency (%)
69931
< 0.1%
68531
< 0.1%
67231
< 0.1%
66861
< 0.1%
66611
< 0.1%
66321
< 0.1%
66151
< 0.1%
66061
< 0.1%
66001
< 0.1%
65971
< 0.1%

Wilderness_Area1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
11523 
1
3597 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
011523
76.2%
13597
 
23.8%

Length

2022-05-07T13:28:08.197778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:08.575023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
011523
76.2%
13597
 
23.8%

Most occurring characters

ValueCountFrequency (%)
011523
76.2%
13597
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011523
76.2%
13597
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011523
76.2%
13597
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011523
76.2%
13597
 
23.8%

Wilderness_Area2
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14621 
1
 
499

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014621
96.7%
1499
 
3.3%

Length

2022-05-07T13:28:09.183651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:09.472324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014621
96.7%
1499
 
3.3%

Most occurring characters

ValueCountFrequency (%)
014621
96.7%
1499
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014621
96.7%
1499
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014621
96.7%
1499
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014621
96.7%
1499
 
3.3%

Wilderness_Area3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
8771 
1
6349 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08771
58.0%
16349
42.0%

Length

2022-05-07T13:28:09.712808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:10.050025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
08771
58.0%
16349
42.0%

Most occurring characters

ValueCountFrequency (%)
08771
58.0%
16349
42.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08771
58.0%
16349
42.0%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08771
58.0%
16349
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08771
58.0%
16349
42.0%

Wilderness_Area4
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
10445 
1
4675 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010445
69.1%
14675
30.9%

Length

2022-05-07T13:28:10.377917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:10.744106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
010445
69.1%
14675
30.9%

Most occurring characters

ValueCountFrequency (%)
010445
69.1%
14675
30.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010445
69.1%
14675
30.9%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010445
69.1%
14675
30.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010445
69.1%
14675
30.9%

Soil_Type1
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14765 
1
 
355

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014765
97.7%
1355
 
2.3%

Length

2022-05-07T13:28:11.024650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:11.281503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014765
97.7%
1355
 
2.3%

Most occurring characters

ValueCountFrequency (%)
014765
97.7%
1355
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014765
97.7%
1355
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014765
97.7%
1355
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014765
97.7%
1355
 
2.3%

Soil_Type2
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14497 
1
 
623

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014497
95.9%
1623
 
4.1%

Length

2022-05-07T13:28:11.518836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:11.825777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014497
95.9%
1623
 
4.1%

Most occurring characters

ValueCountFrequency (%)
014497
95.9%
1623
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014497
95.9%
1623
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014497
95.9%
1623
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014497
95.9%
1623
 
4.1%

Soil_Type3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14158 
1
 
962

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014158
93.6%
1962
 
6.4%

Length

2022-05-07T13:28:12.142543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:12.496871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014158
93.6%
1962
 
6.4%

Most occurring characters

ValueCountFrequency (%)
014158
93.6%
1962
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014158
93.6%
1962
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014158
93.6%
1962
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014158
93.6%
1962
 
6.4%

Soil_Type4
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14277 
1
 
843

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014277
94.4%
1843
 
5.6%

Length

2022-05-07T13:28:12.826026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:13.181316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014277
94.4%
1843
 
5.6%

Most occurring characters

ValueCountFrequency (%)
014277
94.4%
1843
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014277
94.4%
1843
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014277
94.4%
1843
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014277
94.4%
1843
 
5.6%

Soil_Type5
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14955 
1
 
165

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014955
98.9%
1165
 
1.1%

Length

2022-05-07T13:28:13.496176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:13.837335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014955
98.9%
1165
 
1.1%

Most occurring characters

ValueCountFrequency (%)
014955
98.9%
1165
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014955
98.9%
1165
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014955
98.9%
1165
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014955
98.9%
1165
 
1.1%

Soil_Type6
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14470 
1
 
650

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014470
95.7%
1650
 
4.3%

Length

2022-05-07T13:28:14.106185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:14.359816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014470
95.7%
1650
 
4.3%

Most occurring characters

ValueCountFrequency (%)
014470
95.7%
1650
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014470
95.7%
1650
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014470
95.7%
1650
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014470
95.7%
1650
 
4.3%

Soil_Type7
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15120 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015120
100.0%

Length

2022-05-07T13:28:14.588978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:14.880539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015120
100.0%

Most occurring characters

ValueCountFrequency (%)
015120
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015120
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015120
100.0%

Soil_Type8
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15119 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015119
> 99.9%
11
 
< 0.1%

Length

2022-05-07T13:28:15.147573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:15.395354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015119
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
015119
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015119
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015119
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015119
> 99.9%
11
 
< 0.1%

Soil_Type9
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15110 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015110
99.9%
110
 
0.1%

Length

2022-05-07T13:28:15.616434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:15.863597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015110
99.9%
110
 
0.1%

Most occurring characters

ValueCountFrequency (%)
015110
99.9%
110
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015110
99.9%
110
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015110
99.9%
110
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015110
99.9%
110
 
0.1%

Soil_Type10
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
12978 
1
2142 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012978
85.8%
12142
 
14.2%

Length

2022-05-07T13:28:16.384965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:16.633766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
012978
85.8%
12142
 
14.2%

Most occurring characters

ValueCountFrequency (%)
012978
85.8%
12142
 
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
012978
85.8%
12142
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
012978
85.8%
12142
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
012978
85.8%
12142
 
14.2%

Soil_Type11
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14714 
1
 
406

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014714
97.3%
1406
 
2.7%

Length

2022-05-07T13:28:16.923791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:17.181558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014714
97.3%
1406
 
2.7%

Most occurring characters

ValueCountFrequency (%)
014714
97.3%
1406
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014714
97.3%
1406
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014714
97.3%
1406
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014714
97.3%
1406
 
2.7%

Soil_Type12
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14893 
1
 
227

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014893
98.5%
1227
 
1.5%

Length

2022-05-07T13:28:17.398356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:17.675021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014893
98.5%
1227
 
1.5%

Most occurring characters

ValueCountFrequency (%)
014893
98.5%
1227
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014893
98.5%
1227
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014893
98.5%
1227
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014893
98.5%
1227
 
1.5%

Soil_Type13
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14644 
1
 
476

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014644
96.9%
1476
 
3.1%

Length

2022-05-07T13:28:17.949040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:18.196133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014644
96.9%
1476
 
3.1%

Most occurring characters

ValueCountFrequency (%)
014644
96.9%
1476
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014644
96.9%
1476
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014644
96.9%
1476
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014644
96.9%
1476
 
3.1%

Soil_Type14
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14951 
1
 
169

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014951
98.9%
1169
 
1.1%

Length

2022-05-07T13:28:18.532093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:18.882321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014951
98.9%
1169
 
1.1%

Most occurring characters

ValueCountFrequency (%)
014951
98.9%
1169
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014951
98.9%
1169
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014951
98.9%
1169
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014951
98.9%
1169
 
1.1%

Soil_Type15
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15120 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015120
100.0%

Length

2022-05-07T13:28:19.203964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:19.614548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015120
100.0%

Most occurring characters

ValueCountFrequency (%)
015120
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015120
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015120
100.0%

Soil_Type16
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15006 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015006
99.2%
1114
 
0.8%

Length

2022-05-07T13:28:19.952449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:20.375886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015006
99.2%
1114
 
0.8%

Most occurring characters

ValueCountFrequency (%)
015006
99.2%
1114
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015006
99.2%
1114
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015006
99.2%
1114
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015006
99.2%
1114
 
0.8%

Soil_Type17
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14508 
1
 
612

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014508
96.0%
1612
 
4.0%

Length

2022-05-07T13:28:20.747506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:21.182524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014508
96.0%
1612
 
4.0%

Most occurring characters

ValueCountFrequency (%)
014508
96.0%
1612
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014508
96.0%
1612
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014508
96.0%
1612
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014508
96.0%
1612
 
4.0%

Soil_Type18
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15060 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015060
99.6%
160
 
0.4%

Length

2022-05-07T13:28:21.578644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:21.999465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015060
99.6%
160
 
0.4%

Most occurring characters

ValueCountFrequency (%)
015060
99.6%
160
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015060
99.6%
160
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015060
99.6%
160
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015060
99.6%
160
 
0.4%

Soil_Type19
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15074 
1
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015074
99.7%
146
 
0.3%

Length

2022-05-07T13:28:22.483006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:22.978458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015074
99.7%
146
 
0.3%

Most occurring characters

ValueCountFrequency (%)
015074
99.7%
146
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015074
99.7%
146
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015074
99.7%
146
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015074
99.7%
146
 
0.3%

Soil_Type20
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14981 
1
 
139

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014981
99.1%
1139
 
0.9%

Length

2022-05-07T13:28:23.451800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:23.847633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014981
99.1%
1139
 
0.9%

Most occurring characters

ValueCountFrequency (%)
014981
99.1%
1139
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014981
99.1%
1139
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014981
99.1%
1139
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014981
99.1%
1139
 
0.9%

Soil_Type21
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15104 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015104
99.9%
116
 
0.1%

Length

2022-05-07T13:28:24.172103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:24.648892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015104
99.9%
116
 
0.1%

Most occurring characters

ValueCountFrequency (%)
015104
99.9%
116
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015104
99.9%
116
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015104
99.9%
116
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015104
99.9%
116
 
0.1%

Soil_Type22
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14775 
1
 
345

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014775
97.7%
1345
 
2.3%

Length

2022-05-07T13:28:25.665789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:26.208042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014775
97.7%
1345
 
2.3%

Most occurring characters

ValueCountFrequency (%)
014775
97.7%
1345
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014775
97.7%
1345
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014775
97.7%
1345
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014775
97.7%
1345
 
2.3%

Soil_Type23
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14363 
1
 
757

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014363
95.0%
1757
 
5.0%

Length

2022-05-07T13:28:26.525792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:26.864069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014363
95.0%
1757
 
5.0%

Most occurring characters

ValueCountFrequency (%)
014363
95.0%
1757
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014363
95.0%
1757
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014363
95.0%
1757
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014363
95.0%
1757
 
5.0%

Soil_Type24
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14863 
1
 
257

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014863
98.3%
1257
 
1.7%

Length

2022-05-07T13:28:27.110163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:27.396227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014863
98.3%
1257
 
1.7%

Most occurring characters

ValueCountFrequency (%)
014863
98.3%
1257
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014863
98.3%
1257
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014863
98.3%
1257
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014863
98.3%
1257
 
1.7%

Soil_Type25
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15119 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015119
> 99.9%
11
 
< 0.1%

Length

2022-05-07T13:28:27.660762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:27.988031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015119
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
015119
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015119
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015119
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015119
> 99.9%
11
 
< 0.1%

Soil_Type26
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15066 
1
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015066
99.6%
154
 
0.4%

Length

2022-05-07T13:28:28.315932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:28.662503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015066
99.6%
154
 
0.4%

Most occurring characters

ValueCountFrequency (%)
015066
99.6%
154
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015066
99.6%
154
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015066
99.6%
154
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015066
99.6%
154
 
0.4%

Soil_Type27
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15105 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015105
99.9%
115
 
0.1%

Length

2022-05-07T13:28:28.978673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:29.322385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015105
99.9%
115
 
0.1%

Most occurring characters

ValueCountFrequency (%)
015105
99.9%
115
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015105
99.9%
115
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015105
99.9%
115
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015105
99.9%
115
 
0.1%

Soil_Type28
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15111 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015111
99.9%
19
 
0.1%

Length

2022-05-07T13:28:29.642430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:29.982706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015111
99.9%
19
 
0.1%

Most occurring characters

ValueCountFrequency (%)
015111
99.9%
19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015111
99.9%
19
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015111
99.9%
19
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015111
99.9%
19
 
0.1%

Soil_Type29
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
13829 
1
 
1291

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
013829
91.5%
11291
 
8.5%

Length

2022-05-07T13:28:30.291393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:30.630580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
013829
91.5%
11291
 
8.5%

Most occurring characters

ValueCountFrequency (%)
013829
91.5%
11291
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013829
91.5%
11291
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
013829
91.5%
11291
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
013829
91.5%
11291
 
8.5%

Soil_Type30
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14395 
1
 
725

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
014395
95.2%
1725
 
4.8%

Length

2022-05-07T13:28:30.902432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:31.205519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014395
95.2%
1725
 
4.8%

Most occurring characters

ValueCountFrequency (%)
014395
95.2%
1725
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014395
95.2%
1725
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014395
95.2%
1725
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014395
95.2%
1725
 
4.8%

Soil_Type31
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14788 
1
 
332

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014788
97.8%
1332
 
2.2%

Length

2022-05-07T13:28:31.516134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:31.855510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014788
97.8%
1332
 
2.2%

Most occurring characters

ValueCountFrequency (%)
014788
97.8%
1332
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014788
97.8%
1332
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014788
97.8%
1332
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014788
97.8%
1332
 
2.2%

Soil_Type32
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14430 
1
 
690

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014430
95.4%
1690
 
4.6%

Length

2022-05-07T13:28:32.162059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:32.498128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014430
95.4%
1690
 
4.6%

Most occurring characters

ValueCountFrequency (%)
014430
95.4%
1690
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014430
95.4%
1690
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014430
95.4%
1690
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014430
95.4%
1690
 
4.6%

Soil_Type33
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14504 
1
 
616

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014504
95.9%
1616
 
4.1%

Length

2022-05-07T13:28:32.801329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:33.135334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014504
95.9%
1616
 
4.1%

Most occurring characters

ValueCountFrequency (%)
014504
95.9%
1616
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014504
95.9%
1616
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014504
95.9%
1616
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014504
95.9%
1616
 
4.1%

Soil_Type34
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15098 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015098
99.9%
122
 
0.1%

Length

2022-05-07T13:28:33.723598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:33.981271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015098
99.9%
122
 
0.1%

Most occurring characters

ValueCountFrequency (%)
015098
99.9%
122
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015098
99.9%
122
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015098
99.9%
122
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015098
99.9%
122
 
0.1%

Soil_Type35
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15018 
1
 
102

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015018
99.3%
1102
 
0.7%

Length

2022-05-07T13:28:34.251470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:34.603952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015018
99.3%
1102
 
0.7%

Most occurring characters

ValueCountFrequency (%)
015018
99.3%
1102
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015018
99.3%
1102
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015018
99.3%
1102
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015018
99.3%
1102
 
0.7%

Soil_Type36
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15110 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015110
99.9%
110
 
0.1%

Length

2022-05-07T13:28:34.924244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:35.231008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015110
99.9%
110
 
0.1%

Most occurring characters

ValueCountFrequency (%)
015110
99.9%
110
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015110
99.9%
110
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015110
99.9%
110
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015110
99.9%
110
 
0.1%

Soil_Type37
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
15086 
1
 
34

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015086
99.8%
134
 
0.2%

Length

2022-05-07T13:28:35.457112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:35.715326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
015086
99.8%
134
 
0.2%

Most occurring characters

ValueCountFrequency (%)
015086
99.8%
134
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015086
99.8%
134
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015086
99.8%
134
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015086
99.8%
134
 
0.2%

Soil_Type38
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14392 
1
 
728

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014392
95.2%
1728
 
4.8%

Length

2022-05-07T13:28:35.954885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:36.212058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014392
95.2%
1728
 
4.8%

Most occurring characters

ValueCountFrequency (%)
014392
95.2%
1728
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014392
95.2%
1728
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014392
95.2%
1728
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014392
95.2%
1728
 
4.8%

Soil_Type39
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14463 
1
 
657

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014463
95.7%
1657
 
4.3%

Length

2022-05-07T13:28:36.430670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:36.688389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014463
95.7%
1657
 
4.3%

Most occurring characters

ValueCountFrequency (%)
014463
95.7%
1657
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014463
95.7%
1657
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014463
95.7%
1657
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014463
95.7%
1657
 
4.3%

Soil_Type40
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
0
14661 
1
 
459

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014661
97.0%
1459
 
3.0%

Length

2022-05-07T13:28:36.964070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T13:28:37.300980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
014661
97.0%
1459
 
3.0%

Most occurring characters

ValueCountFrequency (%)
014661
97.0%
1459
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014661
97.0%
1459
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014661
97.0%
1459
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014661
97.0%
1459
 
3.0%

Cover_Type
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2022-05-07T13:28:37.577672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.000066141
Coefficient of variation (CV)0.5000165352
Kurtosis-1.250016528
Mean4
Median Absolute Deviation (MAD)2
Skewness0
Sum60480
Variance4.000264568
MonotonicityNot monotonic
2022-05-07T13:28:37.886854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
52160
14.3%
22160
14.3%
12160
14.3%
72160
14.3%
32160
14.3%
62160
14.3%
42160
14.3%
ValueCountFrequency (%)
12160
14.3%
22160
14.3%
32160
14.3%
42160
14.3%
52160
14.3%
62160
14.3%
72160
14.3%
ValueCountFrequency (%)
72160
14.3%
62160
14.3%
52160
14.3%
42160
14.3%
32160
14.3%
22160
14.3%
12160
14.3%

Interactions

2022-05-07T13:27:51.161137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:26:55.015680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:00.758470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:06.874906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:12.794737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:18.416892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:22.628214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:27.297307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:32.250759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:36.422458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:41.408810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:46.141988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:51.544995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:26:55.560457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:01.135007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:07.446821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:13.195785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:18.794315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:23.030292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:27.647639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:32.577132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:36.817720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:41.803099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:46.547072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:51.969972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:26:56.099160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:01.583671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:08.092399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:13.652703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:19.126223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:23.670984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:28.028729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:32.941373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:37.174884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:42.175942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:46.915147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:52.344456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:26:56.605879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:02.239823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:08.770052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:14.022384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:19.492764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:24.065198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:28.432500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:33.277224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:37.576408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:42.569018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:47.275108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:52.679731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:26:57.127177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:02.663878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:09.268268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:14.549998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:19.862566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:24.489879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:28.803696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:33.601526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:38.001515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:42.975377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:47.773963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:53.044126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:26:57.604074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:03.067492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:09.886550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:15.467360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:20.225229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:24.880333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:29.195024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:33.933367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:38.448240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:43.494665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:48.232244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:53.370575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:26:58.248405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:03.461962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:10.361932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:15.868732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:20.558604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:25.249261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:29.595777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:34.257575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:38.822287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:43.866723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:48.928951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:53.829127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:26:58.690931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:03.975881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:10.872504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:16.260629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:20.884566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:25.560173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:30.026367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:34.591850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:39.216671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:44.308517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:49.305906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:54.176007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:26:59.160873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:04.860433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:11.272741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:16.751952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:21.248379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:25.879525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:30.374701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:34.967276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:39.902275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:44.687211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:49.714726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:54.562639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:26:59.547671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:05.401278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:11.647299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:17.204477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:21.619005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:26.250667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:30.725749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:35.298371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:40.279260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:45.037305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:50.067596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:54.924213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:26:59.930675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:05.870271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:11.987911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:17.610058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:21.914372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:26.575720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:31.135233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:35.629942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:40.608261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:45.416703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:50.474680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:55.315753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:00.385726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:06.425634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:12.391619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:18.010281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:22.300713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:26.952053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:31.899810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:36.009231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:40.998991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:45.783564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T13:27:50.826431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-07T13:28:38.316307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-07T13:28:39.482311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-07T13:28:40.888476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-07T13:28:41.968672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-07T13:28:42.605538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-07T13:27:56.882153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

IdElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40Cover_Type
01259651325805102212321486279100000000000000000000000000000001000000000005
122590562212-63902202351516225100000000000000000000000000000001000000000005
23280413992686531802342381356121100000000000000100000000000000000000000000002
3427851551824211830902382381226211100000000000000000000000000000000100000000002
452595452153-13912202341506172100000000000000000000000000000001000000000005
5625791326300-15672302371406031100000000000000000000000000000001000000000002
67260645727056332222251386256100000000000000000000000000000001000000000005
78260549423475732222301446228100000000000000000000000000000001000000000005
892617459240566662232211336244100000000000000000000000000000001000000000005
91026125910247116362282191246230100000000000000000000000000000001000000000005

Last rows

IdElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40Cover_Type
151101511125083326671644204173911385001000000000010000000000000000000000000000006
1511115112261059176010674231202981328001000000000010000000000000000000000000000006
1511215113260038251240589212178891261001000000000010000000000000000000000000000006
151131511426881041544310805245219991266001000000000001000000000000000000000000000003
1511415115267010812624247302412251121231001000000000001000000000000000000000000000003
151151511626072432325876601702512141282001000010000000000000000000000000000000000003
1511615117260312119633195618249221911325001000010000000000000000000000000000000000003
1511715118249213425365117335250220831187001000010000000000000000000000000000000000003
1511815119248716728218101242229237119932001000010000000000000000000000000000000000003
151191512024751973431978270189244164914001001000000000000000000000000000000000000003